Scenario Tree and Adaptive Decision Making on Optimal Type and Timing for Intervention and Social-economic Activity Changes to Manage the COVID-19 Pandemic

Authors

  • Kyeongah Nah
  • Shengyuan Chen
  • Yanyu Xiao
  • Biao Tang
  • Nicola Bragazzi
  • Jane Heffernan
  • Ali Asgary
  • Nicholas Ogden
  • Jianhong Wu

DOI:

https://doi.org/10.29020/nybg.ejpam.v13i3.3792

Keywords:

Scenario tree, COVID-19 social distancing, lockdown exit strategy, re-opening, transmission dynamics model, stochastic optimization

Abstract

We introduce a novel approach to inform the re-opening plan followed by a postpandemic lockdown by integrating a stochastic optimization technique with a disease transmission model. We assess Ontarios re-opening plans as a case-study. Taking into account the uncertainties in contact rates during different re-opening phases, we find the optimal timing for the upcoming re-opening phase that maximizes the relaxation of social contacts under uncertainties, while not overwhelming the health system capacity before the arrival of effective therapeutics or vaccines.

Downloads

Published

2020-07-31

How to Cite

Nah, K., Chen, S., Xiao, Y., Tang, B., Bragazzi, N., Heffernan, J., Asgary, A., Ogden, N., & Wu, J. (2020). Scenario Tree and Adaptive Decision Making on Optimal Type and Timing for Intervention and Social-economic Activity Changes to Manage the COVID-19 Pandemic. European Journal of Pure and Applied Mathematics, 13(3), 710–729. https://doi.org/10.29020/nybg.ejpam.v13i3.3792

Issue

Vol. 13 No. 3: (July 2020)

Section

Nonlinear Analysis

License

Upon acceptance of an article by the journal, the author(s) accept(s) the transfer of copyright of the article to European Journal of Pure and Applied Mathematics.

European Journal of Pure and Applied Mathematics will be Copyright Holder.